Cigarettes sold in China: design, emissions and metals
Bibliographic record
Abstract
BACKGROUND: China is the home to the world's largest cigarette maker, China National Tobacco Company (CNTC), yet little is known publicly about the design and emissions of Chinese cigarettes. CNTC is currently in the process of consolidating its brands and has ambitions to export its cigarettes. Machine-measured tar yields of many of its cigarette brands have also been reduced, similar to what occurred in Western countries from the 1970s through the 1990s with so-called 'low-tar' cigarettes introduced to address consumer concerns about health risks from smoking. METHOD: The current study examines the design and physical characteristics, labelled smoke emissions and tobacco metals content of leading brands of Chinese cigarettes from seven cities purchased in 2005-6 and in 2007. RESULTS: Findings suggest that similar to most countries, tar levels of Chinese cigarettes are predicted primarily by tobacco weight and filter ventilation. Ventilation explained approximately 50% of variation observed in tar and 60% variation in carbon monoxide yields. We found little significant change in key design features of cigarettes purchased in both rounds. We observed significant levels of various metals, averaging 0.82 μg/g arsenic (range 0.3-3.3), 3.21 μg/g cadmium (range 2.0-5.4) and 2.65 μg/g lead (range 1.2-6.5) in a subsample of 13 brands in 2005-6, substantially higher than contemporary Canadian products. CONCLUSION: Results suggest that cigarettes in China increasingly resemble those sold in Western countries, but with tobacco containing higher levels of heavy metals. As CNTC looks to export its product around the world, independent surveillance of tobacco product characteristics, including tobacco blend characteristics, will become increasingly important.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".